{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "import numpy as np\n",
    "import os\n",
    "import scipy.io as sio\n",
    "import scipy.signal as ssi\n",
    "import sys\n",
    "sys.path.append(\"../../../ecg\")\n",
    "\n",
    "import util"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path = \"/deep/group/awni/ecg_models/default/1527627404-9/0.337-0.880-012-0.255-0.906.hdf5\"\n",
    "data_path = \"/deep/group/med/alivecor/training2017/\"\n",
    "\n",
    "SAMPLE_RATE = 300\n",
    "\n",
    "records = load_all(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "preproc = util.load(os.path.dirname(model_path))\n",
    "model = keras.models.load_model(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6000,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f541fa86f90>]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx = ssi.resample(records[0][0], int((2 / 3.) * records[0][0].shape[0]))\n",
    "plt.plot(nx)\n",
    "print nx.shape\n",
    "plt.plot(records[0][0])\n",
    "#print records[0][0].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "ecgs = []; labels = []\n",
    "for ecg, label in records:\n",
    "    if ecg.shape[0] > 8832:\n",
    "        start = int((ecg.shape[0] - 8832) / 2)\n",
    "        ecgs.append(ecg[start:start+8832]) # TODO take the middle?\n",
    "        labels.append(label)\n",
    "\n",
    "ecgs = ssi.resample(np.stack(ecgs), 5888, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7568/7568 [==============================] - 14s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "x = ecgs.copy()\n",
    "mean, std = np.mean(x), np.std(x)\n",
    "x = ((x - mean) / std).astype(np.float32)\n",
    "x = x[:,:,None]\n",
    "preds = model.predict(x, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "cinc17_classes =  {'A' : 0, '~' : 1, 'N' : 2, 'O' : 3}\n",
    "AF = preds[...,0]\n",
    "NOISE = preds[..., 6]\n",
    "SINUS = preds[..., 7]\n",
    "OTHER = np.sum(preds[..., 1:6], axis=2) + np.sum(preds[..., 8:], axis=2)\n",
    "preds = np.stack([AF, NOISE, SINUS, OTHER], axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6724365750528541\n"
     ]
    }
   ],
   "source": [
    "def get_predictions(preds):\n",
    "    # preds is [example x time x classes]\n",
    "    modes, counts = ss.mode(np.argmax(preds, axis=2), axis=1)\n",
    "    return modes.squeeze()\n",
    "    # Two possible strategies here:\n",
    "    # 1. Vote for the best label\n",
    "    # 2. Just take A if A ocurrs\n",
    "    #    otherwise take O\n",
    "    #    otherwise take N\n",
    "    #    otherwise take ~\n",
    "\n",
    "predictions = get_predictions(preds)\n",
    "ground_truth = [cinc17_classes[l] for l in labels]\n",
    "print np.sum(predictions == ground_truth) / float(len(ground_truth))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          A      0.654     0.853     0.741       648\n",
      "          ~      0.322     0.832     0.465       143\n",
      "          N      0.694     0.950     0.802      4557\n",
      "          O      0.750     0.041     0.077      2220\n",
      "\n",
      "avg / total      0.700     0.672     0.578      7568\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as skm\n",
    "report = skm.classification_report(\n",
    "            ground_truth, predictions,\n",
    "            target_names=['A', '~', 'N', 'O'],\n",
    "            digits=3)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 [2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2] 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f541f36b110>]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = 38\n",
    "print predictions[idx], np.argmax(preds, axis=2)[idx,...], ground_truth[idx]\n",
    "plt.plot(x[idx].squeeze())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
